Past research has proposed numerous hardware prefetching techniques, most of which rely
on exploiting one specific type of program context information (eg, program counter …

This paper presents Voyager, a novel neural network for data prefetching. Unlike previous
neural models for prefetching, which are limited to learning delta correlations, our model can …

Applications extensively use data objects with a regular and fixed layout, which leads to the
recurrence of access patterns over memory regions. Spatial data prefetching techniques …

Machine learning algorithms have shown potential to improve prefetching performance by
accurately predicting future memory accesses. Existing approaches are based on the …

Phase change memory (PCM) is a scalable non-volatile memory technology that has low
access latency (like DRAM) and high capacity (like Flash). Writing to PCM incurs …

With the advent of fast processors, TPUs, accelerators, and heterogeneous architectures,
computation is no longer the only bottleneck. In fact for many applications, speed of …

Dynamic neural network (DyNN) enables high computational efficiency and strong
representation capability. However, training DyNN can face a memory capacity problem …

The rapid development of Big Data coupled with slowing down of Moore's law has made the
memory performance a bottleneck in the von Neumann architecture. Machine learning has …

Computing is bottlenecked by data. Large amounts of application data overwhelm storage
capability, communication capability, and computation capability of the modern machines …

Temporal data prefetchers have the potential to produce significant performance gains by
prefetching irregular data streams. Recent work has introduced a neural model for temporal …